This is a software to extand the capabilities of a USB Switch Hub. It use the protocol DDC/CI to change the input of my monitor. I'm using the UGREEN Switch USB 3.0
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| #!/usr/bin/env bash | |
| set -euo pipefail | |
| DOMAIN="${1:-example.com}" | |
| RELEASE="${CC_RELEASE:-cc-main-2026-jan-feb-mar}" | |
| CACHE="${HOME}/.cache/cc-backlinks/${RELEASE}" | |
| BASE="https://data.commoncrawl.org/projects/hyperlinkgraph/${RELEASE}/domain" | |
| VERTICES="${CACHE}/domain-vertices.txt.gz" | |
| EDGES="${CACHE}/domain-edges.txt.gz" |
A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.
This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.
The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
The repository for the assignment is public and Github does not allow the creation of private forks for public repositories.
The correct way of creating a private frok by duplicating the repo is documented here.
For this assignment the commands are:
- Create a bare clone of the repository.
(This is temporary and will be removed so just do it wherever.)
git clone --bare git@github.com:usi-systems/easytrace.git
| /** | |
| * Shelly Pro 3EM - Net Metering (Saldierung) & Home Assistant Auto-Discovery | |
| * Version: 1.1.8 | |
| * | |
| * DISCLAIMER: | |
| * Use this script entirely at your own risk! I assume absolutely no liability | |
| * for any direct, indirect, or consequential damages. This includes, but is | |
| * not limited to, damage to the Shelly device, any connected electrical | |
| * equipment, other devices in your network, data loss, or system malfunctions. | |
| * By using this script, you acknowledge that you alone are responsible for |